Instructions to use artificialguybr/llama3-8b-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artificialguybr/llama3-8b-sql-create-context with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="artificialguybr/llama3-8b-sql-create-context")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("artificialguybr/llama3-8b-sql-create-context")
model = AutoModelForCausalLM.from_pretrained("artificialguybr/llama3-8b-sql-create-context", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use artificialguybr/llama3-8b-sql-create-context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artificialguybr/llama3-8b-sql-create-context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artificialguybr/llama3-8b-sql-create-context

SGLang

How to use artificialguybr/llama3-8b-sql-create-context with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "artificialguybr/llama3-8b-sql-create-context" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "artificialguybr/llama3-8b-sql-create-context" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/llama3-8b-sql-create-context",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use artificialguybr/llama3-8b-sql-create-context with Docker Model Runner:
```
docker model run hf.co/artificialguybr/llama3-8b-sql-create-context
```

llama3-8b-sql-create-context / README.md

artificialguybr

docs: add missing creator links

75aea3b verified 16 days ago

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	---
	base_model: NousResearch/Meta-Llama-3-8B
	tags:
	- generated_from_trainer
	model-index:
	- name: LLAMA-3-8B-SQL-CREATE-CONTEXT
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
	<details><summary>See axolotl config</summary>

	axolotl version: `0.4.0`
	```yaml
	base_model: NousResearch/Meta-Llama-3-8B
	model_type: LlamaForCausalLM
	tokenizer_type: AutoTokenizer

	load_in_8bit: false
	load_in_4bit: false
	strict: false

	datasets:
	- path: b-mc2/sql-create-context
	type: context_qa.load_v2
	dataset_prepared_path: last_run_prepared
	val_set_size: 0.05
	output_dir: ./out-llama8b-createcontext

	sequence_len: 8192
	sample_packing: true
	pad_to_sequence_len: true

	wandb_project: meta-llama-8b-sql-create-context
	wandb_entity:
	wandb_watch:
	wandb_name:
	wandb_log_model:

	gradient_accumulation_steps: 8
	micro_batch_size: 1
	num_epochs: 3
	optimizer: paged_adamw_8bit
	lr_scheduler: cosine
	learning_rate: 2e-5

	train_on_inputs: false
	group_by_length: false
	bf16: auto
	fp16:
	tf32: false

	gradient_checkpointing: true
	gradient_checkpointing_kwargs:
	use_reentrant: false
	early_stopping_patience:
	resume_from_checkpoint:
	logging_steps: 1
	xformers_attention:
	flash_attention: true

	warmup_steps: 100
	evals_per_epoch: 2
	eval_table_size:
	saves_per_epoch: 1

	---
	### 🌐 Website
	You can find more of my models, projects, and information on my official website:
	- [artificialguy.com](https://artificialguy.com/)


	### 🚀 Prompt Hub
	Need high-quality prompts for image models and LLMs? Explore [findgoodprompt.com](https://findgoodprompt.com).
	### 💖 Support My Work
	If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
	- Patreon: [Support on Patreon](https://www.patreon.com/user?u=81570187)
	- Ko-fi: [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
	- Buy Me a Coffee: [Buy me a Coffee](https://buymeacoffee.com/jvkape)
	debug:
	deepspeed:
	weight_decay: 0.0
	fsdp:
	fsdp_config:
	special_tokens:
	pad_token: <\|end_of_text\|>

	```

	</details><br>

	# LLAMA 3 8B SQL CREATE CONTEXT

	Thanks to [Redmond.ai](https://redmond.ai) for the GPU Support!

	This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0201

	## Model description

	The model is a text-to-SQL language model designed to generate SQL queries from natural language inputs. It takes as input a natural language question and a SQL CREATE TABLE statement as context, and outputs a SQL query that answers the question based on the provided table schema.

	The model is trained on a dataset of 78,577 examples, which combines the WikiSQL and Spider datasets. The dataset is specifically designed to prevent hallucination of column and table names, a common issue in text-to-SQL models. The CREATE TABLE statement provides the necessary context for the model to generate accurate SQL queries without requiring actual rows of data.

	The model is intended to be used in applications where the table schema is known, and the goal is to generate SQL queries that answer specific questions based on that schema. The model can be fine-tuned for specific use cases and SQL dialects.

	## Intended uses & limitations

	Intended uses:

	Generating SQL queries from natural language inputs in applications where the table schema is known
	Supporting data analysis and visualization tasks in various domains
	Integrating with other language models or tools to provide a more comprehensive data analysis pipeline
	Limitations:

	The model relies on the accuracy of the provided CREATE TABLE statement and may not perform well if the schema is incomplete or incorrect
	The model may not generalize well to unseen SQL dialects or table schemas
	The model may not be able to handle complex queries that require multiple joins or subqueries
	The model may not be able to handle queries that require external knowledge or common sense
	The model may not be able to handle queries that are ambiguous or open-ended
	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 0.7175 \| 0.01 \| 1 \| 0.7699 \|
	\| 0.055 \| 0.51 \| 35 \| 0.0394 \|
	\| 0.03 \| 1.01 \| 70 \| 0.0231 \|
	\| 0.0215 \| 1.5 \| 105 \| 0.0203 \|
	\| 0.0185 \| 2.01 \| 140 \| 0.0193 \|
	\| 0.0106 \| 2.5 \| 175 \| 0.0201 \|


	### Framework versions

	- Transformers 4.40.0.dev0
	- Pytorch 2.2.2+cu121
	- Datasets 2.15.0
	- Tokenizers 0.15.0

	<!-- artificialguybr-links:start -->
	### Explore More

	Discover more of my models, projects, and practical AI resources:
	- AI tools directory: [JustGoodAI](https://justgoodai.com/) — a curated directory of useful AI tools.

	<!-- artificialguybr-links:end -->